Mengjie Zhang, Evolutionary Transfer Learning

Evolutionary Scheduling and Combinatorial Optimisation

Scheduling and Combinatorial Optimization is an active research
area in both Artificial Intelligence and Operations Research due to
its applicability and interesting computational aspects. Scheduling
and combinatorial optimization problems include a wide range of
combinatorial optimization and search problems in which the task is
to accommodate a set of entities such as events, items, tasks,
projects, activities, people and vehicles into a pattern of
time-space so that the available resources are utilized as
efficiently as possible and the additional constraints are satisfied.

Evolutionary techniques are suitable for these problems since
they are highly flexible in terms of handling constraints, dynamic
changes and multiple conflicting objectives. Evolutionary methods
have been applied to a number of problems including optimization,
search and design with considerable success. However, there are still
many issues to be investigated in this area.

This project aims to develop new algorithms for evolutionary
scheduling and combinatorial optimisation problems. Specifically, we
would like to develop new algorithms for

A strong background in Java/C/C++ programming and a basic background
in Artificial Intelligence and statistics are required. A good
background in machine learning, and operations research is desired
(COMP307, COMP361).

Please check
http://homepages.ecs.vuw.ac.nz/~mengjie/papers/index.shtml,
http://ecs.victoria.ac.nz/Main/MengjieZhang, and
http://ecs.victoria.ac.nz/Groups/ECRG/ for publications and other
information.